In this paper, we adapt a nature-inspired optimization approach, the water flow algorithm, for solving the quadratic assignment problem. The algorithm imitates the hydrological cycle in meteorology and the erosion phenomenon in nature. In this algorithm, a systematic precipitation generating scheme is included to increase the spread of the raindrop positions on the ground to increase the solution exploration capability of the algorithm. Efficient local search methods are also used to enhance the solution exploitation capability of the algorithm. In addition, a parallel computing strategy is integrated into the algorithm to speed up the computation time. The performance of the algorithm is tested with the benchmark instances of the quadratic assignment problem taken from the QAPLIB. The computational results and comparisons show that our algorithm is able to obtain good quality or optimal solutions to the benchmark instances within reasonable computation time.

Solving QAP relaxation to construct initial solution for simulated annealing depends on the capability of solvers used [37]. Thus, the algorithm may not solve instances of size $n>30$ effectively or extensive computation time may be required.

Although greedy approach improves the quality of individuals, this may affect the overall performance of the genetic algorithm [2]. In addition, using 2-exchange local search could limit the capability for searching better solutions.

Solving QAP relaxation to construct initial solution for simulated annealing depends on the capability of solvers used [37]. Thus, the algorithm may not solve instances of size $n>30$ effectively or extensive computation time may be required.

Although greedy approach improves the quality of individuals, this may affect the overall performance of the genetic algorithm [2]. In addition, using 2-exchange local search could limit the capability for searching better solutions.